Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward.ĭeveloping genomic knowledge bases and databases to support clinical management: current perspectives
We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings.
We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. Huser, Vojtech Sincan, Murat Cimino, James J The implications of results for tool designs are discussed.ĭeveloping genomic knowledge bases and databases to support clinical management: current perspectives. As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling.
But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Supporting cognition in systems biology analysis: findings on users' processes and design implications.Ĭurrent usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses.
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